MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics

Jonathan Strutz, Kevin M. Shebek, Linda J. Broadbelt, Keith E.J. Tyo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

Original languageEnglish (US)
Pages (from-to)3484-3487
Number of pages4
JournalBioinformatics
Volume38
Issue number13
DOIs
StatePublished - Jul 1 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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